Development and evolution of RT ontology for automatic service generation system in Kukanchi

This paper presents a novel approach for RT Ontology development, including ontology learning and evolution mechanism. In service robotics systems, understanding the relationship between everyday objects and user intention is the key feature to provide suitable services according to context. RT Ontology has shown to be an efficient technique to represent this relationship. In the proposed method, text corpus grabbed from search engines and lightweight natural language processing techniques were used for term extraction and enabling RT Ontology automatic creation. On the other hand, ontology evolution mechanism is introduced. With these learning and evolution capabilities, the presented RT Ontology model may adapt dynamically to the changes of environment and human activities. This will help to improve the robustness of current RT service generation systems, while reduce much of required labor work for ontology development. Experiments were conducted to show the effectiveness of proposed method.